Cao Anhua, Nie You, Zhong Zhun, Pei Yi, Deng Ping, Xie Hebin, Leng Yiping
The Affiliated Changsha Central Hospital, Department of Cardiology, Hengyang Medical School, University of South China, Changsha, Hunan, China.
The Affiliated Changsha Central Hospital, Department of Laboratory Medicine, Hengyang Medical School, University of South China, Changsha, Hunan, China.
Front Cell Infect Microbiol. 2025 Aug 11;15:1574824. doi: 10.3389/fcimb.2025.1574824. eCollection 2025.
To investigate the risk factors for all-cause mortality of previously untreated pulmonary tuberculosis patients complicated by hypertension and construct a predictive model.
We retrospectively analyzed the clinical data of inpatients with previously untreated pulmonary tuberculosis complicated by hypertension from 2019 to 2021 in Changsha Central Hospital. Patients' survival status and cardiovascular events were collected through telephone follow-up. LASSO regression was utilized to screen predictive variables, and binary logistic regression identified mortality risk factors. A predictive nomogram model was developed using R software, and its precision and reliability were verified.
Among the 1,014 patients, there were 100 (9.86%) deaths and 82 (8.09%) cardiovascular events. LASSO regression screened out 13 predictive variables. Multivariate logistic regression analysis revealed that smoking history, sputum bacteriology, pleural effusion, coronary heart disease, and chronic kidney disease were independent risk factors. Based on the training set data, a nomogram prognostic model was developed, showing an AUC of 0.712 (95% CI: 0.777-0.847), with 50.0% sensitivity and 84.3% specificity. The model's fit was confirmed through internal and external validations.
The prediction model constructed in this study has high predictive ability and satisfactory clinical efficacy, and can provide an effective individualized prediction tool for assessing all-cause mortality risk in patients with previously untreated pulmonary tuberculosis complicated by hypertension.
探讨初治肺结核合并高血压患者全因死亡的危险因素并构建预测模型。
回顾性分析2019年至2021年长沙市中心医院初治肺结核合并高血压住院患者的临床资料。通过电话随访收集患者生存状况及心血管事件。采用LASSO回归筛选预测变量，二元逻辑回归确定死亡危险因素。使用R软件建立预测列线图模型，并验证其准确性和可靠性。
1014例患者中，死亡100例（9.86%），发生心血管事件82例（8.09%）。LASSO回归筛选出13个预测变量。多因素逻辑回归分析显示，吸烟史、痰菌情况、胸腔积液、冠心病和慢性肾脏病是独立危险因素。基于训练集数据建立列线图预后模型，AUC为0.712（95%CI：0.777 - 0.847），灵敏度为50.0%，特异度为84.3%。通过内部和外部验证证实了模型的拟合度。
本研究构建的预测模型具有较高的预测能力和满意的临床疗效，可为评估初治肺结核合并高血压患者全因死亡风险提供有效的个体化预测工具。
